R-universe search: exports:tpm

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bioc

SingleCellExperiment:S4 Classes for Single Cell Data

Defines a S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metadata for genes and libraries.

Maintained by Davide Risso. Last updated 22 days ago.

immunooncology datarepresentation dataimport infrastructure singlecell

13.53 score 15k scripts 285 dependents

drizopoulos

ltm:Latent Trait Models under IRT

Analysis of multivariate dichotomous and polytomous data using latent trait models under the Item Response Theory approach. It includes the Rasch, the Two-Parameter Logistic, the Birnbaum's Three-Parameter, the Graded Response, and the Generalized Partial Credit Models.

Maintained by Dimitris Rizopoulos. Last updated 3 years ago.

30 stars 9.61 score 1.0k scripts 27 dependents

janoleko

LaMa:Fast Numerical Maximum Likelihood Estimation for Latent Markov Models

A variety of latent Markov models, including hidden Markov models, hidden semi-Markov models, state-space models and continuous-time variants can be formulated and estimated within the same framework via directly maximising the likelihood function using the so-called forward algorithm. Applied researchers often need custom models that standard software does not easily support. Writing tailored 'R' code offers flexibility but suffers from slow estimation speeds. We address these issues by providing easy-to-use functions (written in 'C++' for speed) for common tasks like the forward algorithm. These functions can be combined into custom models in a Lego-type approach, offering up to 10-20 times faster estimation via standard numerical optimisers. To aid in building fully custom likelihood functions, several vignettes are included that show how to simulate data from and estimate all the above model classes.

Maintained by Jan-Ole Koslik. Last updated 2 days ago.

openblas cpp openmp

9 stars 7.86 score 42 scripts

bioc

autonomics:Unified Statistical Modeling of Omics Data

This package unifies access to Statistal Modeling of Omics Data. Across linear modeling engines (lm, lme, lmer, limma, and wilcoxon). Across coding systems (treatment, difference, deviation, etc). Across model formulae (with/without intercept, random effect, interaction or nesting). Across omics platforms (microarray, rnaseq, msproteomics, affinity proteomics, metabolomics). Across projection methods (pca, pls, sma, lda, spls, opls). Across clustering methods (hclust, pam, cmeans). It provides a fast enrichment analysis implementation. And an intuitive contrastogram visualisation to summarize contrast effects in complex designs.

Maintained by Aditya Bhagwat. Last updated 2 months ago.

software dataimport preprocessing dimensionreduction principalcomponent regression differentialexpression genesetenrichment transcriptomics transcription geneexpression rnaseq microarray proteomics metabolomics massspectrometry

5.95 score 5 scripts

erossiter

catSurv:Computerized Adaptive Testing for Survey Research

Provides methods of computerized adaptive testing for survey researchers. See Montgomery and Rossiter (2020) <doi:10.1093/jssam/smz027>. Includes functionality for data fit with the classic item response methods including the latent trait model, Birnbaum`s three parameter model, the graded response, and the generalized partial credit model. Additionally, includes several ability parameter estimation and item selection routines. During item selection, all calculations are done in compiled C++ code.

Maintained by Erin Rossiter. Last updated 11 months ago.

gsl cpp

12 stars 4.68 score 3 scripts

cran

twopartm:Two-Part Model with Marginal Effects

Fit two-part regression models for zero-inflated data. The models and their components are represented using S4 classes and methods. Average Marginal effects and predictive margins with standard errors and confidence intervals can be calculated from two-part model objects. Belotti, F., Deb, P., Manning, W. G., & Norton, E. C. (2015) <doi:10.1177/1536867X1501500102>.

Maintained by Yajie Duan. Last updated 2 years ago.

3 stars 2.18 score